{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 包导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "护石:\n",
      "   等级      一技能  Lv    二技能  Lv1   孔  value\n",
      "0   7  火属性攻击强化   1     不屈  1.0  21      2\n",
      "1   6     昏厥耐性   2    击晕术  1.0  11      3\n",
      "2   5      植生学   1  毒属性强化  2.0  11      1\n",
      "3   6     风压耐性   1    击晕术  1.0  11      1\n",
      "4   7       逆袭   2     利刃  1.0   1      4\n",
      "基本信息: \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1499 entries, 0 to 1498\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   等级      1499 non-null   int64  \n",
      " 1   一技能     1499 non-null   object \n",
      " 2   Lv      1499 non-null   int64  \n",
      " 3   二技能     1341 non-null   object \n",
      " 4   Lv1     1341 non-null   float64\n",
      " 5   孔       1499 non-null   int64  \n",
      " 6   value   1499 non-null   int64  \n",
      "dtypes: float64(1), int64(4), object(2)\n",
      "memory usage: 82.1+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "stone  = pd.read_excel(\"stone.xlsx\")\n",
    "print(\"护石:\")\n",
    "print(stone.head())\n",
    "print(\"基本信息: \")\n",
    "print(stone.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "技能:\n",
      "  LookUpName  MaxLevel\n",
      "0         攻击         7\n",
      "1        挑战者         5\n",
      "2         无伤         3\n",
      "3         怨恨         5\n",
      "4       死里逃生         3\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 112 entries, 0 to 111\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   LookUpName  112 non-null    object\n",
      " 1   MaxLevel    112 non-null    int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 1.9+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "skill = pd.read_excel(\"skill.xlsx\")\n",
    "print(\"技能:\")\n",
    "print(skill.head())\n",
    "print(skill.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>等级</th>\n",
       "      <th>一技能</th>\n",
       "      <th>Lv</th>\n",
       "      <th>二技能</th>\n",
       "      <th>Lv1</th>\n",
       "      <th>孔</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   等级  一技能  Lv  二技能  Lv1   孔  value\n",
       "0   7   13   1   92  1.0  21      2\n",
       "1   6   76   2   41  1.0  11      3\n",
       "2   5   79   1   18  2.0  11      1\n",
       "3   6   62   1   41  1.0  11      1\n",
       "4   7  106   2   23  1.0   1      4\n",
       "5   4   40   3   16  1.0   0      4\n",
       "6   6   67   2   69  1.0  21      1\n",
       "7   5   70   1   13  2.0  11      2\n",
       "8   5   18   2   53  1.0   1      1\n",
       "9   7   76   1   62  1.0  21      2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 处理缺失值\n",
    "stone.fillna({\"二技能\":\"无\",\"Lv.1\":0},inplace=True)\n",
    "\n",
    "#处理技能,将技能名称用数字替换\n",
    "skillArray = skill[\"LookUpName\"].values\n",
    "stone['一技能'].replace(skillArray,list(range(1,113)),inplace=True)\n",
    "stone['二技能'].replace(skillArray,list(range(1,113)),inplace=True)\n",
    "stone.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看各个value的护石的占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "font1 = {'family' : 'Times New Roman',\n",
    "'weight' : 'normal',\n",
    "'size' : 15,\n",
    "}\n",
    "\n",
    "#饼状图\n",
    "fig,ax=plt.subplots(1,2,figsize=(15,8))\n",
    "stone['value'].value_counts().plot.pie(ax=ax[0],shadow=True,autopct='%.2f%%')\n",
    "ax[0].set_ylabel('')\n",
    "ax[0].set_xlabel('value',font1)\n",
    "#柱状图\n",
    "stone['value'].value_counts().plot.bar(ax=ax[1])\n",
    "ax[1].set_ylabel('number',font1)\n",
    "ax[1].set_xlabel('value',font1)\n",
    "#设置相关信息\n",
    "plt.xticks(fontsize=15,rotation=0)\n",
    "plt.yticks(fontsize=15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每种等级的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7    476\n",
      "5    421\n",
      "6    326\n",
      "4    194\n",
      "3     82\n",
      "Name: 等级, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每种等级的数量\n",
    "lv_value = stone[\"等级\"].value_counts()\n",
    "print(lv_value)\n",
    "plt.bar(stone[\"等级\"].unique(),lv_value)\n",
    "plt.xlabel(\"等级\")\n",
    "plt.ylabel(\"数量\")\n",
    "plt.title(\"每个等级的护石数量\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "等级与value的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#等级的列表,value的列表\n",
    "lvList = stone[\"等级\"].unique()\n",
    "valueList = [1,2,3,4,5]\n",
    "#绘制\n",
    "fig,ax= plt.subplots(nrows=5, ncols=2, figsize = (10,20))\n",
    "num = 0\n",
    "for i in lvList:\n",
    "    valueConut = []\n",
    "    for j in valueList:\n",
    "        temp = stone[(stone[\"等级\"]==i) & (stone[\"value\"]==j)]\n",
    "        tempCount = temp[\"value\"].count()\n",
    "        valueConut.append(tempCount)\n",
    "    ax[num,0].bar(valueList,valueConut)\n",
    "    ax[num,0].set_xlabel('value'); \n",
    "    ax[num,0].set_ylabel('数量'); \n",
    "    ax[num,0].set_title(\"等级\"+str(i)+\"中各个value的数量分布\")\n",
    "\n",
    "    ax[num,1].pie(valueConut,labels = valueList,autopct='%.2f%%',labeldistance=1.1)\n",
    "    ax[num,1].set_title(\"等级\"+str(i)+\"中各个value的占比\")\n",
    "    # print(valueConut)\n",
    "    num+=1\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "value与技能的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>一技能</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.216144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.227481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               一技能\n",
       "count  1499.000000\n",
       "mean     57.216144\n",
       "std      29.227481\n",
       "min       1.000000\n",
       "25%      35.000000\n",
       "50%      61.000000\n",
       "75%      78.000000\n",
       "max     108.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stone[['一技能']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stone.groupby('一技能')['value'].sum()\n",
    "plt.figure(figsize = (50,6))\n",
    "plt.bar(stone[\"一技能\"].unique(),stone.groupby('一技能')['value'].sum())\n",
    "plt.xticks(fontsize=25,rotation=0)\n",
    "plt.yticks(fontsize=25)\n",
    "plt.title(\"value与一技能之间的关系\",fontsize = 30)\n",
    "plt.xlabel(\"一技能\",fontsize = 30)\n",
    "plt.ylabel(\"value为1的数量\",fontsize = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>二技能</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1499.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>61.923949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.414655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>89.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               二技能\n",
       "count  1499.000000\n",
       "mean     61.923949\n",
       "std      32.414655\n",
       "min       1.000000\n",
       "25%      39.000000\n",
       "50%      63.000000\n",
       "75%      89.000000\n",
       "max     112.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stone[['二技能']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stone.groupby('二技能')['value'].sum()\n",
    "plt.figure(figsize = (50,6))\n",
    "plt.bar(stone[\"二技能\"].unique(),stone.groupby('二技能')['value'].sum())\n",
    "plt.xticks(fontsize=25,rotation=0)\n",
    "plt.yticks(fontsize=25)\n",
    "plt.title(\"value与二技能之间的关系\",fontsize = 30)\n",
    "plt.xlabel(\"二技能\",fontsize = 30)\n",
    "plt.ylabel(\"value为1的数量\",fontsize = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "孔与value的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stone.groupby('孔')['value'].sum()\n",
    "plt.figure(figsize = (50,6))\n",
    "plt.bar(stone[\"孔\"].unique(),stone.groupby('孔')['value'].sum())\n",
    "plt.xticks(fontsize=25,rotation=0)\n",
    "plt.yticks(fontsize=25)\n",
    "plt.title(\"value与孔之间的关系\",fontsize = 30)\n",
    "plt.xlabel(\"孔\",fontsize = 30)\n",
    "plt.ylabel(\"value为1的数量\",fontsize = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 降维 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>等级</th>\n",
       "      <th>孔</th>\n",
       "      <th>value</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.848808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1.505373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.289074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.690824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2.214343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.738088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.068941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.147280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.167078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.952777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1499 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      等级   孔  value      temp\n",
       "0      7  21      2 -1.848808\n",
       "1      6  11      3  1.505373\n",
       "2      5  11      1 -0.289074\n",
       "3      6  11      1 -0.690824\n",
       "4      7   1      4  2.214343\n",
       "...   ..  ..    ...       ...\n",
       "1494   5  11      1 -0.738088\n",
       "1495   5   0      1 -1.068941\n",
       "1496   7  31      2 -0.147280\n",
       "1497   4   1      3  2.167078\n",
       "1498   4   0      1  1.952777\n",
       "\n",
       "[1499 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "lda = LDA(n_components=1)\n",
    "pca = PCA(n_components=1)\n",
    "skill1=stone[['一技能','Lv']]\n",
    "re_1=lda.fit_transform(skill1,stone['一技能'])\n",
    "skill2=stone[['二技能','Lv1']]\n",
    "re_2=lda.fit_transform(skill1,stone['二技能'])\n",
    "skill=np.concatenate((re_1,re_2),axis=1)\n",
    "re_skill=pca.fit_transform(skill)\n",
    "stone=pd.concat((stone,pd.DataFrame(re_skill,columns=['temp'])),axis = 1)\n",
    "stone.drop(['一技能', 'Lv','二技能','Lv1'], axis=1,inplace=True)\n",
    "stone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 归一化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>等级</th>\n",
       "      <th>孔</th>\n",
       "      <th>value</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.147585</td>\n",
       "      <td>0.352653</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.468677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.319755</td>\n",
       "      <td>0.012831</td>\n",
       "      <td>3</td>\n",
       "      <td>1.195855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.508074</td>\n",
       "      <td>0.012831</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.229638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.319755</td>\n",
       "      <td>0.012831</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.548784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.147585</td>\n",
       "      <td>-0.326990</td>\n",
       "      <td>4</td>\n",
       "      <td>1.759054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1494</th>\n",
       "      <td>-0.508074</td>\n",
       "      <td>0.012831</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.586331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>-0.508074</td>\n",
       "      <td>-0.360972</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.849157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>1.147585</td>\n",
       "      <td>0.692474</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.116998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>-1.335904</td>\n",
       "      <td>-0.326990</td>\n",
       "      <td>3</td>\n",
       "      <td>1.721508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>-1.335904</td>\n",
       "      <td>-0.360972</td>\n",
       "      <td>1</td>\n",
       "      <td>1.551269</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1499 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            等级         孔  value      temp\n",
       "0     1.147585  0.352653      2 -1.468677\n",
       "1     0.319755  0.012831      3  1.195855\n",
       "2    -0.508074  0.012831      1 -0.229638\n",
       "3     0.319755  0.012831      1 -0.548784\n",
       "4     1.147585 -0.326990      4  1.759054\n",
       "...        ...       ...    ...       ...\n",
       "1494 -0.508074  0.012831      1 -0.586331\n",
       "1495 -0.508074 -0.360972      1 -0.849157\n",
       "1496  1.147585  0.692474      2 -0.116998\n",
       "1497 -1.335904 -0.326990      3  1.721508\n",
       "1498 -1.335904 -0.360972      1  1.551269\n",
       "\n",
       "[1499 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "kong=stone['孔'].values.reshape(-1,1)\n",
    "temp=stone['temp'].values.reshape(-1,1)\n",
    "rank=stone['等级'].values.reshape(-1,1)\n",
    "\n",
    "scaler = StandardScaler(copy=False)\n",
    "stone['孔']=scaler.fit_transform(kong)\n",
    "stone['temp']=scaler.fit_transform(temp)\n",
    "stone['等级']=scaler.fit_transform(rank)\n",
    "stone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 包导入和训练数据的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((899, 3), (899,), (600, 3), (600,))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn import svm \n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics \n",
    "from sklearn.metrics import confusion_matrix \n",
    "\n",
    "target = 'value'\n",
    "x_columns=[x for x in stone.columns if x not in [target]]\n",
    "X = stone[x_columns]\n",
    "Y = stone['value']\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.4)\n",
    "x_train.shape,y_train.shape,x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测模型 rbf svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for rbf SVM is 0.6366666666666667\n"
     ]
    }
   ],
   "source": [
    "model=svm.SVC(kernel='rbf',C=1,gamma=0.1)\n",
    "model.fit(x_train,y_train)\n",
    "prediction1=model.predict(x_test)\n",
    "print('Accuracy for rbf SVM is',metrics.accuracy_score(prediction1,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测模型 Linear svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for linear SVM is 0.6066666666666667\n"
     ]
    }
   ],
   "source": [
    "model=svm.SVC(kernel='linear',C=0.1,gamma=0.1)\n",
    "model.fit(x_train,y_train)\n",
    "prediction2=model.predict(x_test)\n",
    "print('Accuracy for linear SVM is',metrics.accuracy_score(prediction2,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测模型 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression is 0.6333333333333333\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(x_train,y_train)\n",
    "prediction3=model.predict(x_test)\n",
    "print('The accuracy of the Logistic Regression is',metrics.accuracy_score(prediction3,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测模型 DecisionTree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree is 0.6616666666666666\n"
     ]
    }
   ],
   "source": [
    "model=DecisionTreeClassifier()\n",
    "model.fit(x_train,y_train)\n",
    "prediction4=model.predict(x_test)\n",
    "print('The accuracy of the Decision Tree is',metrics.accuracy_score(prediction4,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测模型 KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN is 0.6683333333333333\n"
     ]
    }
   ],
   "source": [
    "model=KNeighborsClassifier() \n",
    "model.fit(x_train,y_train)\n",
    "prediction5=model.predict(x_test)\n",
    "print('The accuracy of the KNN is',metrics.accuracy_score(prediction5,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测模型 KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracies for different values of n are: [0.62666667 0.67166667 0.685      0.67       0.66833333 0.665\n",
      " 0.64833333 0.65833333 0.66166667 0.65166667] \n",
      "with the max value as  0.685\n"
     ]
    }
   ],
   "source": [
    "a_index=list(range(1,11))\n",
    "a=pd.Series()\n",
    "x=[0,1,2,3,4,5,6,7,8,9,10]\n",
    "for i in list(range(1,11)):\n",
    "    model=KNeighborsClassifier(n_neighbors=i) \n",
    "    model.fit(x_train,y_train)\n",
    "    prediction=model.predict(x_test)\n",
    "    a=a.append(pd.Series(metrics.accuracy_score(prediction,y_test)))\n",
    "plt.plot(a_index, a)\n",
    "plt.xticks(x)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(12,6)\n",
    "plt.show()\n",
    "print('Accuracies for different values of n are:',a.values,'\\nwith the max value as ',a.values.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测模型 高斯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the NaiveBayes is 0.6433333333333333\n"
     ]
    }
   ],
   "source": [
    "model=GaussianNB()\n",
    "model.fit(x_train,y_train)\n",
    "prediction6=model.predict(x_test)\n",
    "print('The accuracy of the NaiveBayes is',metrics.accuracy_score(prediction6,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测模型 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Random Forests is 0.6583333333333333\n"
     ]
    }
   ],
   "source": [
    "model=RandomForestClassifier(n_estimators=100)\n",
    "model.fit(x_train,y_train)\n",
    "prediction7=model.predict(x_test)\n",
    "print('The accuracy of the Random Forests is',metrics.accuracy_score(prediction7,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CV Mean</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Svm</th>\n",
       "      <td>0.626474</td>\n",
       "      <td>0.052518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radial Svm</th>\n",
       "      <td>0.672483</td>\n",
       "      <td>0.053271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.669803</td>\n",
       "      <td>0.049770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.687150</td>\n",
       "      <td>0.039650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.660416</td>\n",
       "      <td>0.054685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Naive Bayes</th>\n",
       "      <td>0.638434</td>\n",
       "      <td>0.047897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.657776</td>\n",
       "      <td>0.047578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CV Mean       Std\n",
       "Linear Svm           0.626474  0.052518\n",
       "Radial Svm           0.672483  0.053271\n",
       "Logistic Regression  0.669803  0.049770\n",
       "KNN                  0.687150  0.039650\n",
       "Decision Tree        0.660416  0.054685\n",
       "Naive Bayes          0.638434  0.047897\n",
       "Random Forest        0.657776  0.047578"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold #for K-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score #score evaluation\n",
    "# from sklearn.model_selection import cross_val_predict #prediction\n",
    "kfold = KFold(n_splits=10) # k=10, split the data into 10 equal parts\n",
    "xyz=[]\n",
    "accuracy=[]\n",
    "std=[]\n",
    "\n",
    "classifiers=['Linear Svm','Radial Svm','Logistic Regression','KNN','Decision Tree','Naive Bayes','Random Forest']\n",
    "models=[svm.SVC(kernel='linear'),svm.SVC(kernel='rbf'),LogisticRegression(),KNeighborsClassifier(n_neighbors=9),\n",
    "        DecisionTreeClassifier(),GaussianNB(),RandomForestClassifier(n_estimators=100)]\n",
    "for i in models:\n",
    "    model = i\n",
    "    cv_result = cross_val_score(model,X,Y, cv = kfold,scoring = \"accuracy\")\n",
    "    cv_result=cv_result\n",
    "    xyz.append(cv_result.mean())\n",
    "    std.append(cv_result.std())\n",
    "    accuracy.append(cv_result)\n",
    "new_models_dataframe2=pd.DataFrame({'CV Mean':xyz,'Std':std},index=classifiers)       \n",
    "new_models_dataframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12,6))\n",
    "box=pd.DataFrame(accuracy,index=[classifiers])\n",
    "box.T.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_models_dataframe2['CV Mean'].plot.barh(width=0.8)\n",
    "plt.title('Average CV Mean Accuracy')\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(8,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "f,ax=plt.subplots(3,3,figsize=(12,10))\n",
    "y_pred = cross_val_predict(svm.SVC(kernel='rbf'),X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[0,0],annot=True,fmt='2.0f',cmap='coolwarm')\n",
    "ax[0,0].set_title('Matrix for rbf-SVM')\n",
    "y_pred = cross_val_predict(svm.SVC(kernel='linear'),X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[0,1],annot=True,fmt='2.0f',cmap='coolwarm')\n",
    "ax[0,1].set_title('Matrix for Linear-SVM')\n",
    "y_pred = cross_val_predict(KNeighborsClassifier(n_neighbors=9),X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[0,2],annot=True,fmt='2.0f',cmap='coolwarm')\n",
    "ax[0,2].set_title('Matrix for KNN')\n",
    "y_pred = cross_val_predict(RandomForestClassifier(n_estimators=100),X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[1,0],annot=True,fmt='2.0f',cmap='coolwarm')\n",
    "ax[1,0].set_title('Matrix for Random-Forests')\n",
    "y_pred = cross_val_predict(LogisticRegression(),X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[1,1],annot=True,fmt='2.0f',cmap='coolwarm')\n",
    "ax[1,1].set_title('Matrix for Logistic Regression')\n",
    "y_pred = cross_val_predict(DecisionTreeClassifier(),X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[1,2],annot=True,fmt='2.0f',cmap='coolwarm')\n",
    "ax[1,2].set_title('Matrix for Decision Tree')\n",
    "y_pred = cross_val_predict(GaussianNB(),X,Y,cv=10)\n",
    "sns.heatmap(confusion_matrix(Y,y_pred),ax=ax[2,0],annot=True,fmt='2.0f',cmap='coolwarm')\n",
    "ax[2,0].set_title('Matrix for Naive Bayes')\n",
    "plt.subplots_adjust(hspace=0.3,wspace=0.5)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "36bc871987b263becf5d2db01c0cab90f45da526e4522ff2e0a513b58fb5e03b"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
